Clustering analysis using Swarm Intelligence

This thesis is concerned with the application of the swarm intelligence methods in clustering analysis of datasets. The main objectives of the thesis are ∙ Take the advantage of a novel evolutionary algorithm, called artificial bee colony, to improve the capability of K-means in finding global optimum clusters in nonlinear partitional clustering problems. ∙ Consider partitional clustering as an optimization problem and an improved antbased algorithm, named Opposition-Based API (after the name of Pachycondyla APIcalis ants), to automatic grouping of large unlabeled datasets. ∙ Define partitional clustering as a multiobjective optimization problem. The aim is to obtain well-separated, connected, and compact clusters and for this purpose, two objective functions have been defined based on the concepts of data connectivity and cohesion. These functions are the core of an efficient multiobjective particle swarm optimization algorithm, which has been devised for and applied to automatic grouping of large unlabeled datasets. For that purpose, this thesis is divided is five main parts: ∙ The first part, including Chapter 1, aims at introducing state of the art of swarm intelligence based clustering methods. ∙ The second part, including Chapter 2, consists in clustering analysis with combination of artificial bee colony algorithm and K-means technique. ∙ The third part, including Chapter 3, consists in a presentation of clustering analysis using opposition-based API algorithm. ∙ The fourth part, including Chapter 4, consists in multiobjective clustering analysis using particle swarm optimization. ∙ Finally, the fifth part, including Chapter 5, concludes the thesis and addresses the future directions and the open issues of this research

Clustering analysis using Swarm Intelligence

This thesis is concerned with the application of the swarm intelligence methods in clustering analysis of datasets. The main objectives of the thesis are ∙ Take the advantage of a novel evolutionary algorithm, called artificial bee colony, to improve the capability of K-means in finding global optimum clusters in nonlinear partitional clustering problems. ∙ Consider partitional clustering as an optimization problem and an improved antbased algorithm, named Opposition-Based API (after the name of Pachycondyla APIcalis ants), to automatic grouping of large unlabeled datasets. ∙ Define partitional clustering as a multiobjective optimization problem. The aim is to obtain well-separated, connected, and compact clusters and for this purpose, two objective functions have been defined based on the concepts of data connectivity and cohesion. These functions are the core of an efficient multiobjective particle swarm optimization algorithm, which has been devised for and applied to automatic grouping of large unlabeled datasets. For that purpose, this thesis is divided is five main parts: ∙ The first part, including Chapter 1, aims at introducing state of the art of swarm intelligence based clustering methods. ∙ The second part, including Chapter 2, consists in clustering analysis with combination of artificial bee colony algorithm and K-means technique. ∙ The third part, including Chapter 3, consists in a presentation of clustering analysis using opposition-based API algorithm. ∙ The fourth part, including Chapter 4, consists in multiobjective clustering analysis using particle swarm optimization. ∙ Finally, the fifth part, including Chapter 5, concludes the thesis and addresses the future directions and the open issues of this research

A Community-based Cloud Computing Caching Service

Caching has become an important technology in the development of cloud computing-based high-performance web services. Caches reduce the request to response latency experienced by users, and reduce workload on backend databases. They need a high cache-hit rate to be fit for purpose, and this rate is dependent on the cache management policy used. Existing cache management policies are not designed to prevent cache pollution or cache monopoly problems, which impacts negatively on the cache-hit rate. This paper proposes a community-based caching approach (CC) to address these two problems. CC was evaluated for performance against thirteen commercially available cache management policies, and results demonstrate that the cache-hit rate achieved by CC was between 0.7% and 55% better than the alternate cache management policies

Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs

Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method

Chemical and biological reactions of solidification of peat using ordinary portland cement (OPC) and coal ashes

Construction over peat area have often posed a challenge to geotechnical engineers. After decades of study on peat stabilisation techniques, there are still no absolute formulation or guideline that have been established to handle this issue. Some researchers have proposed solidification of peat but a few researchers have also discovered that solidified peat seemed to decrease its strength after a certain period of time. Therefore, understanding the chemical and biological reaction behind the peat solidification is vital to understand the limitation of this treatment technique. In this study, all three types of peat; fabric, hemic and sapric were mixed using Mixing 1 and Mixing 2 formulation which consisted of ordinary Portland cement, fly ash and bottom ash at various ratio. The mixtures of peat-binder-filler were subjected to the unconfined compressive strength (UCS) test, bacterial count test and chemical elemental analysis by using XRF, XRD, FTIR and EDS. Two pattern of strength over curing period were observed. Mixing 1 samples showed a steadily increase in strength over curing period until Day 56 while Mixing 2 showed a decrease in strength pattern at Day 28 and Day 56. Samples which increase in strength steadily have less bacterial count and enzymatic activity with increase quantity of crystallites. Samples with lower strength recorded increase in bacterial count and enzymatic activity with less crystallites. Analysis using XRD showed that pargasite (NaCa2[Mg4Al](Si6Al2)O22(OH)2) was formed in the higher strength samples while in the lower strength samples, pargasite was predicted to be converted into monosodium phosphate and Mg(OH)2 as bacterial consortium was re-activated. The Michaelis�Menten coefficient, Km of the bio-chemical reaction in solidified peat was calculated as 303.60. This showed that reaction which happened during solidification work was inefficient. The kinetics for crystallite formation with enzymatic effect is modelled as 135.42 (1/[S] + 0.44605) which means, when pargasite formed is lower, the amount of enzyme secretes is higher

Is swarm intelligence able to create mazes?

In this paper, the idea of applying Computational Intelligence in the process of creation board games, in particular mazes, is presented. For two different algorithms the proposed idea has been examined. The results of the experiments are shown and discussed to present advantages and disadvantages